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You're reading from R Data Mining Implement data mining techniques through practical use cases and real-world datasets

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781787124462

Length 442 pages

Edition 1st Edition

Languages

Tools

ggplot

Concepts

Data Mining

Author (1):

Andrea Cirillo

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Table of Contents (16) Chapters

Preface

1. Why to Choose R for Your Data Mining and Where to Start

2. A First Primer on Data Mining Analysing Your Bank Account Data FREE CHAPTER

3. The Data Mining Process - CRISP-DM Methodology

4. Keeping the House Clean – The Data Mining Architecture

5. How to Address a Data Mining Problem – Data Cleaning and Validation

6. Looking into Your Data Eyes – Exploratory Data Analysis

7. Our First Guess – a Linear Regression

8. A Gentle Introduction to Model Performance Evaluation

9. Don't Give up – Power up Your Regression Including Multiple Variables

10. A Different Outlook to Problems with Classification Models

11. The Final Clash – Random Forests and Ensemble Learning

12. Looking for the Culprit – Text Data Mining with R

13. Sharing Your Stories with Your Stakeholders through R Markdown

14. Epilogue

15. Dealing with Dates, Relative Paths and Functions

Defining a data modelling strategy

I was perhaps too hasty proposing this solution to Mr Clough—he is a great professional, but I have never heard of one of his requests going unsatisfied. Moreover, his words made me think is not excluding the hypothesis of fraud. And this makes me even more nervous, if that's possible.

Nevertheless, we have to do this as if it is business as usual. The point is that we need some kind of data related to default events in the past and the companies that experimented this status. What? They also gave you a dataset about past default events? And you already cleaned it? That is what I call good news. OK, just send it to me and we can start to work on it immediately.

clean_casted_stored_data_validated_complete, uh? You don't fear long names, do you? Just run glimpse on it and see what is inside:

glimpse(clean_casted_stored_data_validated_complete)

Observations: 11,523
 Variables: 16
 $ attr_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...